Despite significant progress, tracking is still considered to be a very challenging task. Recently, the increasing popularity
of depth sensors has made it possible to obtain reliable
depth easily. This may be a game changer for tracking,
since depth can be used to prevent model drift and handle
occlusion. We also observe that current tracking algorithms
are mostly evaluated on a very small number of
videos collected and annotated by different groups. The lack
of a reasonable size and consistently constructed benchmark
has prevented a persuasive comparison among different
algorithms. In this paper, we construct a unified
benchmark dataset of 100 RGBD videos with high diversity,
propose different kinds of RGBD tracking algorithms
using 2D or 3D model, and present a quantitative comparison
of various algorithms with RGB or RGBD input.
We aim to lay the foundation for further research in both
RGB and RGBD tracking, and our benchmark is available at http://tracking.cs.princeton.edu.